I am a Reader in Geospatial Data Science at the University of Glasgow. My research connects GeoAI, geographical information science and urban analytics, developing spatially aware and explainable methods to understand cities and regions.
With my students and collaborators, I investigate how urban form, infrastructure networks and digital platforms relate to mobility, accessibility, inequality, and economic and environmental outcomes. My work combines machine learning with spatial theory, spatial econometrics and network science, linking methodological development to substantive questions in urban and regional research.
Ph.D. in Geography (GIScience)
University of Georgia, USA
MSc. in Geography (GIScience)
University of Georgia, USA
BSc. in Regional Planning
Nanjing University, China
Our mission is to advance GeoAI that connects predictive modelling with explanation grounded in spatial theory. We aim to build transparent, computationally efficient representations of urban and regional systems that help researchers and planners understand spatial inequalities and assess development choices.

Mingshu Wang marks his first career-long inclusion and fifth consecutive annual inclusion in the Stanford–Elsevier lists, ranking in the top 0.53% for annual citation impact within Geological & Geomatics Engineering.
Meet the doctoral researchers, visiting scholars and collaborators contributing to our work in GeoAI and urban analytics.